The dynamics of pathogens spreading through a population of susceptible hosts can be complicated by a number of factors. One of them is pathogen interaction during co-infection. Here infection by one pathogen can change host susceptibility to a second, or being co-infected can change a host's infectivity compared to a singly infected host. A second factor is that infection can alter behavior both for biological reasons (for example, when sickness makes a host less active), and, in humans especially, for social reasons (for example, when sick people self-isolate). These behavioral responses, in turn, change the patterns of interactions that drive transmission dynamics. A third closely related factor is that patterns of spatial aggregation around environmental features like food and water, or for humans, institutions like schools and home, can create an intricate network of interactions that strongly affect how infections spread. The long-term goal of this research is to develop predictive mathematical models and inferential tools for understanding how the dynamics of co- infecting pathogens depend on the interplay of pathogen interactions, behavioral responses, and population substructure. In Aim 1, an agent-based model that includes biological mechanisms of infection and co-infection will be developed. The model will be modular so that complexities can be added and subtracted. In Aim 2, their individual and combined effects will be studied by simulation. Aim 2 will also focus on inference and prediction: approximate Bayesian computation techniques will be used to make inferences about model mechanisms given empirical data which will then be used to predict future infection dynamics. In Aim 3, the agent-based model will be extended to incorporate behavioral responses to infection and population structures that alter the types and frequencies of interactions between individuals. The methods of Aim 2 for doing comparative simulations and statistical inferences will be reapplied to these more complex models of Aim 3. Upon completion, this project will result in a modular array of agent-based models that are both flexible and can be extended in the future to include factors like the spread of opinions in a social network. It will result in genuine insights into how fundamental mechanisms like pathogen interaction during co-infection and behavioral responses can alter pathogen dynamics. It will result in computational tools for using data to do model inference and predict future dynamics. It will also lead to an honest appraisal of where the limits of doing this inference lie and, consequently, how to use detailed experimental work like that exemplified by projects 1 and 2 to improve the process. Ultimately, these tools and insights will put public health and policy professionals in a stronger, more informed position for making decisions.